Marketing 101 principles: Right Product, Right Promotion, Right Timing.
As a marketing leader, you are not here to learn anything you’re already aware of. You are adept as a marketer with a good understanding of the business objectives, rationale, and motivations in crafting the marketing strategy.
Identifying the business goals – Category expansion, new product introductions, clearing excess inventories, expanding market share are all valid business objectives that drive market strategy. What products to sell or feature; when to sell and for what price and promotion, etc, follows next and is all a part of your marketing strategy and planning, that you probably do very well already. So what is missing here?
The missing link is the lack of alignment of the marketing strategy with the required finesse in the marketing execution.
Three distinct components of a Marketing campaign from my perspective:
1) The Marketing Strategy: The first component is a Marketing strategy, design, plan, and rationale (mostly art, common sense and derived from the broad business understanding and available business reports.)
- What channel is best for executing the campaign?
- What products do I need to feature to do category expansion?
- Is the timing right for the new product introduction? For instance, late fall is the right time to launch winter clothing. Early spring is good to launch outdoor products etc.
- Is there a need for reducing excess inventory? What products do I need to include in a clearance sale?
- Pricing based on product positioning, quality, costs, features, and benefits.
- What promotion options do I have at my disposal, to get the required lift?
- You are also probably looking to target-specific segment(s) of your customer base: Customers who buy at full price. Customers who like specific promotions. Holiday spenders. Big spenders. Loyalists
2) Marketing Campaign execution. This should be all science and data-driven. Effective campaign execution is aided by advanced analytics and predictive Customer insights about their journey, behavior, motivation. All of the data-driven analysis work towards a single objective: Build a list of customers that are most likely to meet your marketing strategy.
So, what’s missing from your design that we feel a need to repeat the same marketing principles when identifying the lists?
- Your intent, your marketing strategy, and planning are NOT MET with effective marketing execution.
- What you identified as your business plan will come to fruition when each of those choices you identified during the planning phase, is fully actioned upon – That is, identifying the right target customer, the right product offered, right promotional offer at the right timing.
That’s the catch! In today’s post, let’s explore what it means to match and what it means to bring those right principles at the execution as well
3) Marketing mechanics of Campaign circulation: Choose a method and send the Campaign (email or social media or direct mail)
- This is fairly straightforward. Pick any of the marketing campaign senders like Sendinblue, MailChimp, Klaviyo, etc, to schedule or send all the identified lists with your creatives, messaging using any of these mechanics of the Send process.
How are Campaign lists typically created?
Building campaign lists as some mechanical task isn’t hard. You could build them with any criteria and/or any hypothesis, any hunch as well, for that matter. But, it is like throwing darts at the wall and hoping something will stick.
The approach lacks the promise of tangible results or hard dollar financial impact, confidence in spending marketing dollars and suffer from an inability to show any credible reason as to why those lists are the best for the current marketing campaign. Most importantly, a non-scientific, rudimentary, shallow analysis, will lack the very fundamental marketing principles.
A quick view of how a good marketing strategy meeting with a good calibrated, predictive insight based execution yields the best possible results of high value and high impact for your business.
A few ways of list building methodologies using non-scientific approaches and their negative impact:
- Shallow analysis and hypothesis: This typically involves pulling up the last 60-90 day Sales history and making up some random criteria
- For example, random criteria could be to think someone who purchased the last 3-6 months ago will make a good list.
- This is a baseless hypothesis but because some default sales conversions have been happening for the business, it feels like this kind of hypothesis is valid. This grossly lacks the ability to bring the full potential from your existing Customers’ understanding.
- Basic RFM segmentation: Include some basic RFM segmentation that is superficial with the assumption that people who bought more will buy more.
- RFM: Recency – how recently purchased, Frequency – how many times they purchased and Monetary value – what was the total $ the customer spent so far.
- Create some random segmented Customer lists using a combination of the R, F, and M, and hope it will work.
- Use Rule-based Segmentation: Use some filters based on the activity on the website – Browsing activity, placing items in the abandoned carts etc. This segmentation shows a narrow part of “what” happened and wouldn’t have the power of action guidance as you wouldn’t know “why” they each ended up in those segments. And creating lists based on a simple “what” happened should be based on some random hypothesis, based on a hunch.
What ails the current method of identifying the customers to target?
Each of the approaches suffers from the same aforementioned shortcomings. – Someone is making huge assumptions about the likelihood of purchase and some hypotheses that can neither be argued with logical reasoning nor backed up by any data analysis.
Consider the following:
- People who “abandoned carts” do so for a variety of reasons. Some have developed cold feet, some might have got a better price, some found better products, some didn’t like the estimated ship dates, some didn’t find the complementary product that they wanted to buy together and so on. Each of those customers has potentially traversed through a different journey, had a different opinion of the brand.
- If all you are doing is sending a discount coupon, or a reminder, without knowing the underlying reason, then you are doing a huge disservice to yourself.
Business collateral damage:
None of the available approaches satisfy the need to apply the right foundational marketing principles all the way to the execution. It is clear that none of the above ways of building lists is effective because those approaches are not equipped to understand the deeply hidden patterns and extract the “why” behind all “what” happened to effectively identify the right Customer insights that matter to your execution.
Resulting negative business Impact:
- No confidence in the lists.
- No accountability if they don’t return any ROI.
- No ROI
- Incorrect strategic decisions
- It spins into a negative, vicious cycle and chain of events due to incorrect conclusions of the causal analysis.
Getting on to the virtuous cycle:
Let’s look at how the most ideal data-driven Customer lists are created for the best possible results. Let’s see what are those set of predictive insights that are fully equipped to fulfill our objective of the best marketing execution with the right targeted customer, right timing, the right product featured and right promotion offered to make maximize the sales opportunity. This includes Identifying advanced insights like the average time between purchases coupled with powerful, actionable predictive customer insights.
When properly executed, Predictive Analytics will provide you futuristic insights that are actionable. Your customers don’t purchase whatever you decide to offer and hence understanding how the purchase likelihood should be honed in, to execute the campaign with the right product, right timing and right promotion matters next.
- Immediate Potential: What’s the Customers’ expected dollar spend in the future 1 or 3 or 6 months? This is also often referred to as Predictive Lifetime Value: What is the remaining lifetime value of each of my customers?
- Predictive Lifetime Duration: How long the Customer is predicted to stay active?
- Customer Lifecycle: Where is the Customer currently in the lifecycle stage of budding, growth, maturation, decline, at-risk and already inactive
- Identifying the right customer: What’s the predictive likelihood of purchase if a marketing campaign is sent? This needs evaluating dozens of signals, and relevant, strongly correlated factors, predicting if a purchase is likely at every Customer level.
- What’s the strength of likelihood- Is it most likely, 50-50, unlikely, most unlikely? What’s the degree of confidence of the Customer likely to purchase?
- Right Products: What Product(s) interest each of my Customers?
- This is not a simple extrapolation or based on an unproven hypothesis. This is about a deeper data analytical exercise and about predicting the likelihood of what other similar Products they will likely purchase given their historical purchase, behavioral patterns. Identifying Cross-sell and Up-sell opportunities.
- Right Promotion: What Promotional offers are preferred or liked by each of my Customers? What’s the right level to incentivize to tip them over to make a sale happen? What’s their willingness and interest in redeeming any coupons or offers? This is a feasible scientific analysis that can arm you to influence the sales.
- Right creatives and messaging: With the help of all the set of above deep, actionable insights, you would now know the characteristics of the Customer lists. Now you can make the message highly personalized with the right creatives, messaging promotional offer.
In a nutshell, your marketing execution is only as good as the Customer insights you get. Having done much hard work, thought and analysis on the marketing strategy, don’t let it loose on the execution of it. Identifying the right lists for the right reasons when building your campaign targeted lists is where the traction for your sales and effectiveness of marketing ROI is. When your strategy is matched with effective execution fueled by predictive analytical based Customer journey and lifecycle insights, it will be a transformative growth and experience.
Thanks for reading the post! I’d love to hear your experience validating the premise or otherwise. Please comment or share who might benefit from this perspective.
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